Abstract Castor oil maleate is used in healthcare products, synthetic lubricants, drying oils, water-soluble paints and as a monomer in several polymers. This compound is formed by direct reaction between castor oil and maleic anhydride, which can be accelerated using free radical catalysts. In this work, the influence of processing temperature and the concentration of di-tert-butyl peroxide (DTBP) in the production of castor oil maleate was studied applying response surface methodology. The results showed that the use of the radical initiator increased the rate of reaction, leading to a shorter reaction time and higher productivity. The optimal operating condition was found at 120ºC, 1 mol of maleic anhydride/mol of castor oil and 0.005 mol of DTBP/mol of castor oil, yielding 90.2% of castor oil maleate in 90 min.
The development of polymer resins can benefit from the application of neural networks, using its great ability to correlate inputs and outputs. In this work we have developed a procedure that uses neural networks to correlate the end-user properties of a polymer with the polymerization reactor's operational condition that will produce that desired polymer. This procedure is aimed at speeding up the development of new resins and help finding the appropriate operational conditions to produce a given polymer resin; reducing experimentation, pilot plant tests and therefore time and money spent on development. The procedure shown in this paper can predict the reactor's operational condition with an error lower than 5%.